Appendix II: NPCC 2015 technical details.

Abstract

Annals of the New York Academy of SciencesVolume 1336, Issue 1 p. 116-150 Original ArticleFree Access Appendix II: NPCC 2015 Technical Details First published: 16 February 2015 https://doi.org/10.1111/nyas.12670AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat A. Climate observations and projections: Methods and analyses8 Contents A.1 Observed extreme events A.2 Global climate models (GCMs) A.3 Climate projections Observed extreme events Temperature Hot days, heat waves, and cold days in Central Park (1900–2013) based on maximum temperatures at or above 90°F, 100°F, at or above 90°F for three consecutive days, and minimum temperatures at or below 32°F (see Fig. A.1). Precipitation Heavy precipitation events in Central Park (1900–2013) based on daily precipitation at or above 1, 2, and 4 inches (see Fig. A.2). Tropical storms and hurricanes See Table A.1. Global climate models See Table A.2 for a list of the global climate models used in the NPCC 2015 report. Climate projections Methods for 2100 projections Projections for 2100 require a different approach from the 30-year timeslices (10-year for sea level rise) that are centered on the 2020s, 2050s, and 2080s, which are what the New York City Panel on Climate Change (NPCC) traditionally uses. The primary difference is that because the vast majority of climate model simulations end in 2100, it is not possible to make a projection for the 30-year timeslice (10-year for sea level rise) centered on the year 2100. Given this model availability constraint, the NPCC considered the alternate approaches listed below to generate projections for 2100. Both approaches share one thing in common: they involve adding a linear trend to the final timeslice (2080s for temperature and precipitation, 2090s for sea level rise), and extrapolating that trend to 2100. The final period linear trend (FPLT) is for 2085 to 2099 for temperature and precipitation, and 2095 to 2099 for sea level rise. The NPCC also considered quadratic trends as well, but determined that over the short time periods used for the trends, a linear approach produced comparable results. The two used approaches are: Add each representative concentration pathway (RCP) ensemble mean FPLT to the final timeslice projections for the corresponding RCP, and calculate the four distribution points (i.e., 10th, 25th, 75th, and 90th percentiles). Add the FPLT from each individual model and RCP to the final timeslice for the corresponding model and RCP, and then calculate the four distribution points (i.e., 10th, 25th, 75th, and 90th percentiles). Approaches 1 and 2 were averaged to generate projections for 2100 (Table A.3). Table A.1. Dates and major impacts from tropical storms and hurricanes that struck in New York metropolitan area Central Wind Date Name Categorya pressureb,c speedb,c Notes September 23, 1815 Great September Gale of 1815 3 (155) September 3, 1821 Norfolk and Long Island Hurricane 3 975 (970) 110 (130) Only direct strike on New York City. Surge of 13 ft in 1 hour. Flooded parts of lower Manhattan as far north as Canal Street. September 15, 1858 New England Storm 1 979 (976) 85 (100) September 8, 1869 Eastern New England Storm 2 963 (950) 100 (115) August 23, 1893 Midnight Storm 1 986 (952) 85 (115) Flooded southern Brooklyn and Queens. September 21, 1938 Long Island Express/New England Storm 3 945 (935) 110 (160) Killed ∼700 people. Storm surge of 10–12 feet on Long Island. September 15, 1944 Great Atlantic Hurricane of 1944 1 965 (943) 80 (140) Landfall over central Long Island. August 31, 1954 Carol 2 975 (970) 100 (100) Wind gusts between 115 and 125 mph over eastern Long Island. September 12, 1960 Donna 2–3 965 (932) 110 (160) Storm surge of 11 ft. Second highest recorded water level at the Battery (7.22 ft NAVD88). Lower Manhattan to West & Cortland Streets flooded nearly waist deep. September 21, 1961 Esther 3 978 (927) 115 (145) Minor flooding and power outages disrupted transportation on Long Island. June 22, 1972 Agnes TS – 1 980 (977) 70 (85) Caused significant flooding. August 10, 1976 Belle 1 980 (957) 85 (120) Landfall on Long Island with wind gusts over 95 mph. September 27, 1985 Gloria 2 951 (920) 105 (145) Wind gusts over 110 mph. Struck at low tide with 5.45 ft water level (NAVD88). August 19, 1991 Bob 2 962 (950) 105 (115) Eye passed just east of Long Island. September 16, 1999 Floyd TS – 1 974 (921) 70 (155) Major inland flooding with 24-hour rainfall totals between 10 and 15 inches in upstate New Jersey and New York. August 28, 2011 Irene TS 965 (942) 65 (120) Center passed over Coney Island; 3–6 ft surge. Major inland flooding upstate NY and New England. October 29, 2012 Sandy PTSd–1 946 (941) 80 (115) Major coastal flooding and power outages in New York City, New Jersey, and Long Island coasts. Record maximum water level of 11.28 ft above NAVD88 at the Battery. Note: The above-mentioned storms have been selected based on their tracks and impacts on the New York metropolitan area. No single metric (i.e., location of landfall within a given distance of the city) was used to determine what storms to include. a Category (based on the Saffir–Simpson Scale) is the estimated strength of the storm as it impacted the New York City area. b Minimum central pressure (in millibars (mb)) and maximum wind speed (in miles per hour (mph)). c The central pressure and wind speed at the time the storm impacted the area; the numbers in parenthesis are the storm's most intense observation(s). d PTS, posttropical storm. The term posttropical is used in National Weather Service advisory products to refer to any closed low-pressure system that no longer qualifies as a tropical cyclone (TC). However, such systems can continue carrying heavy rains and damaging winds. Post-TCs can be either frontal (extratropical) or nonfrontal lows. Source: Unisys Hurricane Archive (http://weather.unisys.com/hurricane/). Table A.2. IPCC AR5 global climate models (GCMs) used by the NPCC2 Atmospheric resolution Modeling center Institute ID Model name (lat° × lon°) Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia CSIRO – BOM ACCESS1.0 ACCESS1.3 1.25 × 1.875 1.25 × 1.875 Beijing Climate Center, China Meteorological Administration BCC BCC-CSM1.1 BCC-CSM1.1 (m) 2.8 × 2.8 1.1 × 1.1 College of Global Change and Earth System Science, Beijing Normal University GCESS BNU-ESM 2.8 × 2.8 Canadian Centre for Climate Modelling and Analysis CCCMA CanESM2 2.8 × 2.8 National Center for Atmospheric Research NCAR CCSM4 0.9 × 1.25 Community Earth System Model Contributors NSF-DOE-NCAR CESM1(BGC) CESM1(CAM5) 0.9 × 1.25 0.9 × 1.25 Centro Euro-Mediterraneo per l Cambiamenti Climatici CMCC CMCC-CM CMCC-CMS 0.75 × 0.75 1.9 × 1.9 Centre National de Recherches Météorologiques/Centre Européen de Recherche et Formation Avancée en Calcul Scientifique CNRM-CEFRACS CNRM-CM5 1.4 × 1.4 Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence CSIRO-QCCE CSIRO-Mk3.6.0 1.9 × 1.9 LASG, Institute of Atmospheric Physic, Chinese Academy of Sciences and CESS, Tsinghua University LASG-CESS FGOALS-g2 2.8 × 2.8 The First Institute of Oceanography, SOA, China FIO FIO-ESM 2.8 × 2.8 NOAA Geophysical Fluid Dynamics Laboratory NOAA GFDL GFDL-CM3 GFDL-ESM2G GFDL-ESM2M 2.0 × 2.5 2.0 × 2.5 2.0 × 2.5 NASA Goddard Institute for Space Studies NASA GISS GISS-E2-H GISS-E2-R 2.0 × 2.5 2.0 × 2.5 National Institute of Meteorological Research/Korea Meteorological Administration NIMR/KMA HadGEM2-AO 1.25 × 1.875 Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais) MOHC (additional realizations by INPE) HadGEM2-CC HadGEM2-ES 1.25 × 1.875 1.25 × 1.875 Institute for Numerical Mathematics INM INM-CM4 1.5 × 2.0 Institut Pierre-Simon Laplace IPSL IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR 1.9 × 3.75 1.3 × 2.5 1.9 × 3.75 Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies) MIROC MIROC-ESM MIROC-ESM-CHEM 2.8 × 2.8 2.8 × 2.8 Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology MIROC MIROC5 1.4 × 1.4 Max Planck Institute for Meteorology MPI-M MPI-ESM-MR MPI-ESM-LR 1.9 × 1.9 1.9 × 1.9 Meteorological Research Institute MRI MRI-CGCM3 1.1 × 1.1 Norwegian Climate Centre NCC NorESM1-M NorESM1-ME 1.9 × 2.5 1.9 × 2.5 Note: This table provides information about the 35 GCMs used by the NPCC2. The 35 models were developed by 22 modeling centers (left column). Some centers support multiple GCMs, and/or versions (for example, some institutions conducted multiple simulations at varying spatial resolutions) of their GCM. Table A.3. NPCC2 2100 projections for temperature, precipitation, and sea level rise Low estimate Middle range High estimate (10th percentile) (25th to 75th percentile) (90th percentile) (a) Temperature projections for 2100a Approach 1 +4.5°F +6.0 to 10.4°F +11.9°F Approach 2 +3.9°F +5.5 to 10.3°F +12.3°F 2100 Projections (average of Approaches 1 and 2) +4.2°F +5.8 to 10.4°F +12.1°F (b) Precipitation projections for 2100b Approach 1 −1% +2 to +14% +18% Approach 2 −11% −5 to +24% +32% 2100 Projections (average of Approaches 1 and 2) −6% −1 to +19% +25% (c) Sea-level rise projections for 2100c,d Approach 1 7 inches 9 to 18 inches 24 inches Approach 2 6 inches 9 to 19 inches 26 inches Model-based component average 6 inches 9 to 18 inches 25 inches 2100 Total SLR projections (average of Approaches 1 and 2) 15 inches 22 to 50 inches 75 inches a Based on 35 global climate models (GCMs) and two representative concentration pathways (RCPs). Projections are relative to the 1971–2000 base period. b Based on 35 GCMs and two RCPs. Projections are relative to the 1971–2000 base period. c Based on 24 GCMs and two RCPs. Projections are relative to the 2000–2004 base period. d Rows 1, 2, and 3 are for model-based sea level rise components only; the final row shows row three plus all other sea level change components. It is also important to note that uncertainties are inherently much greater for the end of the century than the mid-century. For example, the RCP runs do not sample all the possible carbon and other biogeochemical cycle feedbacks associated with climate change. Even the few Earth System Models in the Coupled Model Intercomparison Project (CMIP5) used by the NPCC2 may underestimate the potential for increased methane and carbon releases from the Arctic under extreme warming scenarios. More generally, the potential for surprises increases further into the future one considers, such as technological innovations that could remove carbon from the atmosphere. Maps of ensemble mean annual temperature and precipitation change Figures A.3 and A.4 demonstrate that the mean temperature and precipitation projections for the New York metropolitan region are part of a larger regional pattern. Shown are the national and regional changes in temperature and precipitation for the 2050s relative to 1971–2000. These changes are averaged across the 35 GCMs under RCP4.5 (top) and RCP 8.5 (bottom); while the two RCPs differ in the amount of changes projected, the spatial pattern across the United States is similar for both RCPs. Because these maps represent an average across 35 models, they obscure the substantial variations from one model to another that are evident in Table A.2. Figure A.1Open in figure viewerPowerPoint Observed extreme temperature events (1900–2013): (a) maximum temperatures at or above 90°F; (b) maximum temperatures at or above 100°F; (c) heat waves (at or above 90°F for three consecutive days); and (d) minimum temperatures at or below 32°F. Figure A.2Open in figure viewerPowerPoint Observed extreme precipitation events (1900–2013): (a) daily precipitation at or above 1 inch; (b) daily precipitation at or above 2 inches; and (c) daily precipitation at or above 4 inches. Figure A.3Open in figure viewerPowerPoint Annual temperature changes in the 2050s. Figure A.4Open in figure viewerPowerPoint Annual precipitation changes in the 2050s. Temperature New York City's proximity to the coast is projected to lead to approximately 0.5°F less warming than in the interior regions of the Northeast. The map also reveals that the Northeast is expected to experience slightly more warming than the mid-Atlantic. Precipitation Precipitation projections also show very little spatial variation across the Northeastern United States. However, the map does reveal a tendency for slightly greater precipitation increases to the north of New York City near the Canadian border, and slightly smaller increases in the mid-Atlantic region. Seasonal and monthly projections Throughout the 21st century, projected warming is comparable in each of the four seasons for the New York metropolitan region (Tables A.4 and A.5). As the century progresses, precipitation increases become highest during the winter season (Tables A.6 and A.7); for both the 2050s and the 2080s, winter is the only season where the 10th percentile projections show projected increases. This indicates that during the other three seasons, precipitation decreases cannot be ruled out. Model-based range of outcomes (distribution) for temperature changes (°F) in New York City, relative to the 1971–2000 base period for the 2020s, 2050s, and 2080s. Projections are based on 35 GCMs and 2 representative concentrations pathways. Model-based range of outcomes (distribution) for precipitation changes (%) in New York City, relative to the 1971–2000 base period for the 2020s, 2050s, and 2080s. Based on 35 GCMs and 2 representative concentrations pathways. Table A.4. NPCC2 projected seasonal temperature changes (°F) Low estimate Middle range High estimate (10th percentile) (25th to 75th percentile) (90th percentile) (a) 2020s Winter 1.4°F 2.0°F to 3.2°F 3.7°F Spring 1.2°F 1.6°F to 2.7°F 3.1°F Summer 1.8°F 2.1°F to 3.1°F 3.3°F Fall 1.9°F 2.3°F to 3.2°F 3.6°F (b) 2050s Winter 3.1°F 4.2°F to 6.0°F 6.8°F Spring 2.7°F 3.6°F to 5.2°F 6.4°F Summer 3.1°F 4.3°F to 5.8°F 6.6°F Fall 3.6°F 4.3°F to 5.7°F 6.8°F (c) 2080s Winter 3.9°F 5.6°F to 8.8°F 10.5°F Spring 3.7°F 4.7°F to 7.9°F 8.9°F Summer 4.1°F 4.9°F to 9.5°F 10.5°F Fall 3.9°F 5.5°F to 9.2°F 10.8°F Notes: Winter, December to February; Spring, March to May; Summer, June to August; Fall, September to November. Based on 35 GCMs and two representative concentration pathways. Projections are relative to the 1971–2000 base period. Table A.5. Projected monthly temperature changes (°F) Low estimate Middle range High estimate (10th percentile) (25th to 75th percentile) (90th percentile) (a) 2020s January 0.9°F 1.6°F to 3.6°F 4.4°F February 0.8°F 1.6°F to 2.9°F 4.0°F March 0.3°F 1.3°F to 2.7°F 3.6°F April 1.1°F 1.5°F to 2.6°F 3.4°F May 0.9°F 1.4°F to 2.9°F 3.5°F June 1.0°F 1.7°F to 2.8°F 3.3°F July 1.4°F 2.0°F to 3.0°F 3.3°F August 1.5°F 2.2°F to 3.1°F 3.5°F September 1.5°F 2.3°F to 3.2°F 3.7°F October 1.2°F 2.0°F to 3.2°F 3.6°F November 1.1°F 1.8°F to 3.2°F 3.8°F December 0.6°F 1.8°F to 3.5°F 4.3°F (b) 2050s January 2.9°F 3.9°F to 6.1°F 7.0°F February 2.8°F 3.6°F to 5.7°F 6.7°F March 2.6°F 3.6°F to 5.2°F 6.4°F April 2.5°F 3.3°F to 5.1°F 6.5°F May 2.4°F 3.2°F to 5.3°F 6.4°F June 2.5°F 3.8°F to 5.9°F 6.3°F July 2.9°F 4.1°F to 5.9°F 6.8°F August 3.2°F 4.3°F to 6.1°F 7.0°F September 3.3°F 4.4°F to 6.2°F 7.0°F October 3.0°F 4.1°F to 5.9°F 6.8°F November 3.2°F 3.9°F to 5.6°F 6.6°F December 2.8°F 3.7°F to 6.1°F 7.0°F (c) 2080s January 3.4°F 5.6°F to 8.9°F 10.7°F February 3.5°F 5.4°F to 8.4°F 10.0°F March 3.0°F 4.5°F to 7.4°F 8.8°F April 3.6°F 4.7°F to 7.9°F 9.4°F May 3.4°F 4.6°F to 8.0°F 9.2°F June 3.3°F 4.7°F to 8.6°F 9.9°F July 3.6°F 5.0°F to 9.3°F 10.4°F August 4.1°F 5.1°F to 9.6°F 11.3°F September 4.2°F 5.3°F to 9.6°F 11.0°F October 3.7°F 4.9°F to 9.0°F 11.0°F November 3.1°F 4.9°F to 8.4°F 9.9°F December 3.6°F 5.3°F to 8.3°F 10.6°F Note: Based on 35 GCMs and two representative concentration pathways. Projections are relative to the 1971–2000 base period. Table A.6. Projected seasonal precipitation changes (%) Low estimate Middle range High estimate (10th percentile) (25th to 75th percentile) (90th percentile) (a) 2020s Winter −3% +1% to +12% +20% Spring −3% +1% to +9% +15% Summer −5% −1% to +11% +15% Fall −5% −2% to +7% +10% (b) 2050s Winter +2% +7% to +18% +24% Spring −1% +3% to +12% +18% Summer −9% −5% to +11% +18% Fall −2% +1% to +10% +14% (c) 2080s Winter +4% +10% to +25% +33% Spring −1% +4% to +15% +21% Summer −10% −5% to +18% +23% Fall −7% −1% to +11% +18% Notes: Winter, December to February; Spring, March to May; Summer, June to August; Fall, September to November. Based on 35 GCMs and two representative concentration pathways. Projections are relative to the 1971–2000 base period. Table A.7. Projected monthly precipitation changes (%) Low estimate Middle range High estimate (10th percentile) (25th to 75th percentile) (90th percentile) (a) 2020s January −8% −1% to +14% 26% February −9% −2% to +16% 31% March −7% −1% to +12% 19% April −12% −4% to +11% 18% May −13% −6% to +10% 20% June −14% −4% to +9% 18% July −12% −4% to +12% 20% August −7% +1% to +13% 20% September −18% −11% to +7% 14% October −19% −7% to +12% 19% November −9% −4% to +12% 21% December −6% −1% to +12% 20% (b) 2050s January −4% +1% to +25% +35% February −4% +2% to +26% +36% March −3% +1% to +18% +25% April −5% −1% to +14% +26% May −10% −5% to +11% +17% June −14% −5% to +13% +18% July −14% −9% to +10% +23% August −13% −4% to +14% +26% September −20% −8% to +11% +16% October −17% −6% to +14% +22% November −5% −1% to +18% +23% December −8% +4% to +19% +23% (c) 2080s January −4% +7% to +28% +40% February −4% +5% to +28% +44% March −1% +5% to +22% +27% April −10% +2% to +19% +25% May −10% −1% to +14% +22% June −14% −2% to +15% +20% July −16% −9% to +19% +32% August −19% −9% to +20% +35% September −16% −8% to +10% +22% October −20% −9% to +9% +23% November −10% −2% to +21% +29% December −5% +5% to +26% +31% Notes: Based on 35 GCMs and two representative concentration pathways. Projections are relative to the 1971–2000 base period. B. Sea level rise observations and projections: methods and analyses9 Contents B.1 NPCC2 sea level rise methods and projections B.2 Ocean changes B.3 Ice mass change B.4 Vertical land movements—glacial isostatic adjustment (GIA) B.5 Anthropogenic land water storage This section describes the New York City Panel on Climate Change (NPCC2) methodology for projecting future sea level rise in New York City. NPCC2 sea level rise methods and projections The regionalized sea level projection methodology used in NPCC (2010) and Horton et al. (2010) is updated here in NPCC2. Individual sea level rise components are described in Chapter 2, NPCC 2015. NPCC2 sea level projections do not comprise the full range of possible sea level rise contributions, but rather present the estimated 10th, 25th, 75th, and 90th percentile sea level contributions by component; the sum of components at each percentile is used to generate a total sea level rise projection for each percentile. As noted in Chapter 2 (NPCC, 2015), this approach neglects correlations between sea level rise components, which could influence the robustness of the projections.1 The cumulative sea level change STOT (at a given likelihood) in New York City is equal to: (1)where STOT is the change in mean sea level for each component since the base period. SOCEAN refers to ocean changes, SI to ice mass change, SGIA to vertical land movements, and SLWS to anthropogenic land water storage. Uncertainty and confidence in the quantitative ranges of individual terms are assessed using a variety of techniques, including model-based approaches, expert judgment, and literature review. Subsequent sections describe the basis for projections of each component in greater detail. Ocean changes For NPCC2, future thermosteric and dynamic ocean changes are determined using outputs (the variables ZOSTOGA and ZOS in the CMIP5 archive) from 24 CMIP5 GCMs under both RCP4.5 and RCP8.5 (see later), yielding a total of 48 outcomes. As in Yin et al. (2012), dynamic sea level is now defined as the grid point anomaly from the global mean field. (2) ZOSTOGA = global mean sea level rise due to thermal expansion, relative to a 2000 to 2004 baseline; ZOS = local sea level rise due to changes in dynamic ocean height (caused by changes in local ocean density and circulation), relative to the 2000 to 2004 local mean.2 Projections of ZOS, particularly the 75th and 90th percentile, reflect a local sea level rise greater than the global mean. This local anomaly has been linked to a slowdown of the Gulf Stream/Atlantic Meridional Ocean Circulation (AMOC) in some GCMs3 (Yin et al., 2009, 2010; Hu et al., 2009; 2011). A higher-than-average rate of local sea level rise has also been observed in recent decades. Tide gauges along the Atlantic coast show a distinct regional sea level acceleration “hotspot” from Cape Cod to Cape Hatteras since the early 1990s (Sallenger et al., 2012; Boon, 2012; Ezer and Corlett, 2012), although the record is still too short to attribute to climate change because of high interannual to multidecadal ocean variability. Ice mass change In NPCC2, sea level rise contributions from four separate ice masses—the Greenland (GR), West Antarctic (WAIS), and East Antarctic (EAIS) ice sheets, and small glaciers and ice caps (GICs)—are projected independently. At each percentile (10th, 25th, 75th, and 90th), the sea level rise due to changes in ice mass balance at New York City (SI) is given by the sum of mass changes in each component (where Mx is expressed in sea level equivalent (360 gigatonne mass loss = 1 mm sea level rise) and fx is the local “fingerprint” of ice mass loss): (3) The subsections below discuss the projections of the individual terms in Eq. 3 in more detail. Mass balance of the Greenland and Antarctic ice sheets Processes that modify continental ice sheet mass balance (and thus their effect of sea level) can be segregated into those that act on an ice sheet's surface mass balance (or SMB, including snow accumulation, melting, and sublimation) and those that affect ice flow (dynamic changes). Recent observations indicate that dynamic changes underlie virtually all of recently observed mass changes in Antarctica, and approximately half in Greenland (Rignot et al., 2011). Because robust, process model-based projections of ice sheet contributions to sea level rise are still under development and a complete quantitative assessment is currently unavailable, NPCC2 utilizes the projections of Bamber and Aspinall (2013) for the Greenland and Antarctic ice sheets. Although this study relies exclusively on expert elicitation, it provides a consistent, probabilistic approach for each ice sheet that includes a combined estimate of uncertainty in SMB and ice dynamics. Box B.1. What is glacial isostatic adjustment (GIA)? GIAs derive from changes in the size of large ice masses, which distort the Earth's lithosphere and change the elevation of the land surface relative to the ocean. Regions formerly beneath ice sheets around 20,000 years ago (e.g., central Canada and Scandinavia) are still uplifting, while peripheral regions (e.g., New York down to Chesapeake Bay) are subsiding in response to the slow, viscous component of glacial isostatic rebound. GIA models (such as ICE-5G v1.3 VM2_L90; Peltier, 2012, 2004; see also Mitrovica and Milne, 2003) calculate gravitational interactions among ice sheets, land, and ocean over time and separate effects of glacial loading/unloading on sea level from the climatic signal. Specific GIA correctionsa for NYC area tide gauges are listed in Table B.1. aNote: These GIA corrections apply to the last deglaciation, not to future ice melting. Mass balance of glaciers and small ice caps4 Projections of the future sea level rise contribution of GICs have been made using: (1) extrapolations of observed rates of mass change (Bahr et al., 2009); (2) regional, process-based, mass balance models forced by GCMs (Radic et al., 2013; Marzeion et al., 2012), and (3) a statistical approach, whereby mass or volume changes are parameterized as a function of climate (e.g., global mean temperature; Perrette et al., 2013, and references therein). We use the process-based approach of Radic et al. (2013) and Marzeion et al. (2014), since it does not rely on stationarity as the climate system, and the GICs, evolves over this century. Fingerprints Land-based ice compresses the lithosphere, exerts a gravitational pull on the surrounding ocean, and alters the Earth's rotation. Localized ice mass changes thus give a spatially varying pattern of sea level change that is known as a “fingerprint” (Tamisiea and Mitrovica, 2011; Mitrovica et al., 2009, 2001; see Eq. 3). For NPCC2, the value of the fingerprint for each ice component in New York City is included as a multiplier of mass change. Here, we assign a single value estimated from the literature (e.g., Mitrovica et al., 2009; Perrette et al., 2013; Gomez et al., 2010; Miller et al., 2013). Vertical land movements—glacial isostatic adjustment (GIA) Vertical land motion in New York City today is primarily “slow” GIA-related subsidence (see Box B.1). Other causes of local vertical land movements (neo-tectonic activity, sediment loading and compaction, and subsidence due to excess subsurface fluid withdrawal) are expected to remain negligible at the Battery in New York City. Table B.1. New York metropolitan region sea level rise and land subsidence NOAA PSMSL Peltier Englehart Engelhart & Horton SLR SLR GIA (2009) Paleo-SLR (2012 Paleo-SLR Station (in/year)a Years (in/year)b Years (in/year)c (in/year)d (in/year)d New London 0.10 73 0.10 68 0.04 0.04 ∼0.04 Bridgeport 0.11 47 0.10 43 0.04 0.04 ∼0.04 Montauk 0.12 64 0.12 53 0.05 0.03 ∼0.04 Port Jefferson 0.10 35 0.09 31 0.05 0.03 ∼0.04 Willets Point 0.10 80 0.10 65 0.05 0.03 ∼0.04 The Battery/New York City 0.11 155 0.11 138 0.05 0.05 ∼0.05 Sandy Hook 0.16 79 0.16 79 0.05 0.06 ∼0.06 Atlantic City 0.16 100 0.16 100 0.05 0.05 ∼0.06 a http://tidesandcurrents.noaa.gov/sltrends/index.shtml/ (see: updated mean sea level trends; current through 2011). b http://www.psmsl.org/products/trends/trends.txt/ (posted January 16, 2013). c GIA corrections for tide gauges predicted by W.R. Peltier's ICE 5G v 1.3, VM2, with 90 km lithosphere resolution. http://www.psmsl.org/train_and_info/geo.signal/gia/peltier/drsl250.PSMSL.ICE5Gv1.3_VM2_L90_2012b/ (posted August 13, 2012). d Engelhart et al. (2009). e Engelhart and Horton (2012). NPCC2 calculates the future subsidence due to GIA as a linear trend where SGIA is the number of years since the start date (tbase, 2002, average of 2000–2004) times the annual subsidence rate, R, in mm/year: (4) Table B.1 lists annual subsidence rates, R, for individual tide stations in the New York metropolitan area. Current GIA-related subsidence rates are now much improved over earlier values and compare favorably with millennial sea level rise trends in this region (Engelhart and Horton, 2012; Engelhart et al., 2009). Therefore, R = 1.26 mm/year for New York City is used to calculate SGIA. This is roughly 40% of the sea level rise in the observed period. For historical sea level rise trends, see http://tidesandcurrents.noaa.gov/sltrends/index.shtml/. Updated mean sea level rise trends (current through 2011) for each of the New York metro area tide gauge stations are listed in Table B.1. Anthropogenic land water storage Continental water storage fluctuates due to variability in precipitation, and increasingly since the 1950s due to human interventions in the hydrological cycle. By storing water on land, reservoirs have reduced sea level rise by 0.55 mm/year since the 1950s (Chao et al., 2008) and 0.44 mm/year since the 1970s (Church et al., 2011). Conversely, groundwater mining (water withdrawal in excess of natural recharge) raises sea level. We also adopted the IPCC (2013) approach in calculating the contribution of changes in land water storage to sea level rise (Church et al., 2013). Specifically, the NPCC 10th, 25th, 75th, and 90th percentile distribution points were calculated by assuming that IPCC projections of sea level rise are based on a normal distribution. The land water storage rates were treated as linear over time; therefore, the 2020s, 2050s, and 2080s projections could be calculated directly from the IPCC ti